Comparison
featuretools vs awesome-mlops
Verdict
Pick featuretools when tags unique to featuretools: automl, python, scikit-learn, feature-engineering; pick awesome-mlops when tags unique to awesome-mlops: engineering, ml, ai, federated-learning.
Markdown twin · featuretools alternatives · awesome-mlops alternatives
GraphCanon updated today
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Trust & integrity
| Signal | featuretools | awesome-mlops |
|---|---|---|
| Maintenance | Very active (4d since push) As of today · github_public_v1 | Dormant (597d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- featuretools
- An open source python library for automated feature engineering
- awesome-mlops
- A curated list of references for MLOps
Stars
- featuretools
- 7.7k
- awesome-mlops
- 14k
Forks
- featuretools
- 916
- awesome-mlops
- 2.1k
Open issues
- featuretools
- 165
- awesome-mlops
- 42
Language
- featuretools
- Python
- awesome-mlops
- -
Adopt for
- featuretools
- -
- awesome-mlops
- -
Persona
- featuretools
- -
- awesome-mlops
- -
Runtime
- featuretools
- -
- awesome-mlops
- -
License
- featuretools
- BSD-3-Clause
- awesome-mlops
- -
Last pushed
- featuretools
- Jul 7, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- featuretools
- Vector Databases
- awesome-mlops
- Model Training, Vector Databases, Inference & Serving
Trust and health
Maintenance
- featuretools
- Very active (96%)
- awesome-mlops
- Dormant (18%)
Days since push
- featuretools
- 4d
- awesome-mlops
- 597d
Open issues (now)
- featuretools
- 165
- awesome-mlops
- 42
Owner type
- featuretools
- Organization
- awesome-mlops
- User
Full report
- featuretools
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · featuretools: Python runtime · awesome-mlops: Python runtime
Choose featuretools if…
- Tags unique to featuretools: automl, python, scikit-learn, feature-engineering.
- More recently updated (last pushed Jul 7, 2026).
When NOT to use featuretools
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: engineering, ml, ai, federated-learning.
- Also covers Model Training, Inference & Serving.
- More GitHub stars (14k vs 7.7k) - visibility, not fit.
When NOT to use awesome-mlops
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alteryx/featuretools) · observed Jul 11, 2026
- GitHub forks (alteryx/featuretools) · observed Jul 11, 2026
- Last push (alteryx/featuretools) · observed Jul 7, 2026
- License file (BSD-3-Clause) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Jul 11, 2026
- GitHub forks (visenger/awesome-mlops) · observed Jul 11, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: featuretools 7.7k · awesome-mlops 14k (synced Jul 11, 2026).
Common questions
- What is the difference between featuretools and awesome-mlops?
- featuretools: An open source python library for automated feature engineering. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose featuretools over awesome-mlops?
- Choose featuretools over awesome-mlops when Tags unique to featuretools: automl, python, scikit-learn, feature-engineering; More recently updated (last pushed Jul 7, 2026).
- When should I choose awesome-mlops over featuretools?
- Choose awesome-mlops over featuretools when Tags unique to awesome-mlops: engineering, ml, ai, federated-learning; Also covers Model Training, Inference & Serving; More GitHub stars (14k vs 7.7k) - visibility, not fit.
- When should I avoid featuretools?
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- When should I avoid awesome-mlops?
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is featuretools or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (13,952 vs 7,661). Stars measure visibility, not whether either tool fits your constraints.
- Are featuretools and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to featuretools or awesome-mlops?
- GraphCanon lists graph-backed alternatives at featuretools alternatives and awesome-mlops alternatives (featuretools markdown twin, awesome-mlops markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, featuretools or awesome-mlops?
- featuretools: Very active. awesome-mlops: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for featuretools and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: featuretools trust report; awesome-mlops trust report.